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Spatial artifact detection improves the reproducibility of drug screening experiments
Aleksandr Ianevski1,2, Kristen Nader1,2, Swapnil Potdar1
1Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
Iscience
|October 31, 2025
Summary
Systematic errors in drug screening plates reduce data reproducibility. A new control-independent method, normalized residual fit error (NRFE), effectively detects these spatial artifacts, improving drug discovery reliability.
Area of Science:
- Pharmacogenomics
- Drug Discovery
- Computational Biology
Background:
- Reproducible drug screening is crucial for drug discovery and personalized medicine.
- Conventional quality control (QC) methods often fail to detect systematic spatial errors in drug plates.
Purpose of the Study:
- To develop a novel, control-independent QC method to identify systematic artifacts in drug screening experiments.
- To enhance the reliability and reproducibility of drug screening data.
Main Methods:
- Developed a control-independent QC approach using normalized residual fit error (NRFE).
- Analyzed over 100,000 duplicate measurements from the PRISM pharmacogenomic study.
- Integrated NRFE with QC methods to analyze drug-cell line pairs across two datasets (Genomics of Drug Sensitivity in Cancer project).
Main Results:
- NRFE-flagged experiments exhibited a 3-fold decrease in reproducibility among technical replicates.
- Improved cross-dataset correlation from 0.66 to 0.76 by integrating NRFE.
- Demonstrated NRFE's ability to detect systematic spatial errors missed by conventional QC.
Conclusions:
- Normalized residual fit error (NRFE) is a robust metric for identifying systematic artifacts in drug screening.
- The plateQC R package offers a valuable tool for improving drug screening data quality for research and translational applications.
